← Back to all news
arXiv Machine Learning By Xuhui Kang, Sung-Wook Lee, Yan Zhuang, Tianle Zhong, Yen-Ling Kuo

RARA: Robust Policy Fine-Tuning through Retrieval-Anchored Representation Alignment for Robotic Manipulation

Read the original on arXiv Machine Learning →

RARA (Retrieval‑Anchored Representation Alignment) is a two‑stage method for fine‑tuning pre‑trained robot policies on new tasks with few demonstrations. It first retrieves and aligns representations from pre‑training that match the target task’s intent and motion, then fine‑tunes while constraining changes to those retrieved representations. Experiments on Diffusion Policy and GR00T across 13 tasks in simulation and on a real robot show RARA improves in‑distribution success by 9.5 points and out‑of‑distribution success by 13.6 points, outperforming all baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Aug 26

Hierarchical Skill Retrieval for Data-Efficient Adaptation of Vision-Language-Action Models

The paper introduces Hierarchical Skill Retrieval (HSR), a framework that decomposes a target manipulation task into candidate skill sequences and evaluates each plan for semantic plausibility and skill reliability. HSR combines subtask-level language retrieval with behavior-feature reranking to select demonstrations that are both relevant and compatible with the target task, followed by a two-stage pretraining and finetuning pipeline for policy adaptation. Experiments on the LIBERO benchmark and real-world robot tasks show that HSR improves average success rates by 10.3% and 21.3% over the strongest baseline, demonstrating the effectiveness of structured skill-level retrieval for data-efficient Vision‑Language‑Action adaptation.

By Haoran Hao, Shahram Najam Syed, Jeff Schneider, Jeffrey Ichnowski
ragroboticsfine-tuningmultimodalbenchmarks
More like this →
arXiv AI
Aug 19

LoopVLA: Learning Sufficiency in Recurrent Refinement for Vision-Language-Action Models

LoopVLA introduces a recurrent Vision‑Language‑Action architecture that learns to refine multimodal representations, predict actions, and estimate when further refinement is unnecessary. By iteratively applying a shared Transformer block and producing a sufficiency score at each step, it decouples refinement from fixed layer indices and aligns confidence scores with action quality through a self‑supervised objective. Experiments on LIBERO, LIBERO‑Plus, and VLA‑Arena demonstrate that LoopVLA reduces model parameters by 45% and boosts inference throughput up to 1.7× while matching or surpassing strong baselines in task success.

By Boyang Shen, Kaixiang Yang, Hao Wang, Qiuyu Yu, Qiang Xie, Qiang Li, Zhiwei Wang
llmsroboticsefficiencymultimodalsafety
More like this →
arXiv AI
Sep 18

HIL-UMI: Bringing Human-in-the-Loop Post-Training of Vision-Language-Action Models to Universal Manipulation Interface

HIL-UMI is a policy-guided Universal Manipulation Interface that enables robot‑free, human‑in‑the‑loop post‑training of vision‑language‑action models. By querying the current policy during handheld demonstrations and using an Energy Score to detect out‑of‑distribution states, it selectively collects new data and refines a progress‑based advantage estimator. The updated estimator then drives advantage‑conditioned behavioral cloning, improving performance on long‑horizon and precise manipulation tasks while reducing per‑frame collection time compared to HG‑DAgger.

By Zimu Han, Yiming Zeng, Jiyao Zhang, Zihao Zhao, Yuanfei Wang, Yixiang Jin, Shiqi Li, Shuangben Chen, Wei Huang, Ruodai Li, Hui Shen, Hao Dong
roboticsfine-tuningmultimodal
More like this →
arXiv AI
Jun 16

Retrieve, Don't Retrain: Extending Vision Language Action Models to New Tasks at Test Time

arXiv:2606. 15631v1 Announce Type: cross Abstract: Extending a vision-language-action (VLA) policy to a new task typically requires task-specific teleoperated demonstrations and per-task fine-tuning, making adaptation costly in both data collection and compute.

By Jeongeun Park, Juhan Park, Taekyung Kim, Sungjoon Choi, Dongyoon Han, Sangdoo Yun
ragroboticsfine-tuningmultimodal
More like this →
arXiv Computer Vision
Oct 1

Correcting WHERE, Preserving HOW: Compositional Generalization for Vision-Language-Action Models via Referential Guidance

arXiv:2609.38616v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models enable flexible action generation, their generalization across diverse environmental elements, including ma...

By Yanyan Zhang, Disheng Liu, Xinpeng Li, Chaoda Song, Mohsen Hariri, Debargha Ganguly, Wang Yang, Kai Ye, Bryce Grant, Vipin Chaudhary, Yu Yin
roboticsmultimodal
More like this →